{"id":"W4410431408","doi":"10.1073/pnas.2420252122","title":"Time-lagged recurrence: A data-driven method to estimate the predictability of dynamical systems","year":2025,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Ministry of Education - Singapore","keywords":"Predictability; Weighting; Dynamical systems theory; Computer science; Operator (biology); Recurrence quantification analysis; Dynamical system (definition); Representation (politics); Lyapunov exponent; Nonlinear system; Nonlinear dynamical systems; Scale (ratio); Mathematics; Statistical physics; Artificial intelligence; Statistics; Chaotic; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001633518,0.000691029,0.000792254,0.001850877,0.0003160323,0.0008491943,0.001071923,0.000755497,0.001316365],"category_scores_gemma":[0.007357139,0.0003193427,0.0008246707,0.001667291,0.0003572656,0.001078555,0.0008035329,0.001072437,0.0003716677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003151475,"about_ca_system_score_gemma":0.000657298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002774429,"about_ca_topic_score_gemma":0.002466908,"domain_scores_codex":[0.9995394,0.0001845892,0.00004381075,0.0001025572,0.00009971038,0.00002995693],"domain_scores_gemma":[0.9975685,0.00145574,0.0003478175,0.000279346,0.0002743574,0.00007410598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002781961,0.0001574816,0.0127488,0.0003473838,0.0003891016,0.0005101299,0.0002834825,0.630624,0.01326164,0.06602091,0.004849747,0.2705291],"study_design_scores_gemma":[0.000004871877,0.00001853376,0.0004942624,0.000008805901,0.00001175369,0.00003147625,0.000009140085,0.9923577,0.0005127307,0.005771938,0.0007679886,0.00001082922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01657522,0.000325301,0.9817514,0.000135196,0.00005396005,0.00002550025,0.0002721607,0.0003894015,0.0004719115],"genre_scores_gemma":[0.5888323,0.0008394704,0.4060304,0.0001589124,0.0002529681,0.0002396619,0.001482877,0.0002715099,0.001892032],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002774429,"threshold_uncertainty_score":0.008639038,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06455869032052562,"score_gpt":0.3514482723916951,"score_spread":0.2868895820711694,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}